1. Technical Field
The present disclosure relates to segmentation and, more specifically, to hierarchical atlas-based segmentation.
2. Discussion of Related Art
Segmentation relates to the process of dividing a digital image into recognized portions or segments. Segmentation has many applications within the field of computer vision, and in particular, segmentation has proven useful when applied to medical images. For example, segmentation may be used to locate tumors and other pathologies within a computed tomography (CT) study or a magnetic resonance imaging (MRI) scan. However, segmentation may have many other uses such as facial recognition.
There are multiple different approaches for performing image segmentation. One such example is atlas-based segmentation. In atlas-based segmentation, a reference image which is to be segmented is compared to an atlas that is a single representative dataset which includes one or more template images that are fully annotated a-priori by an expert. In comparing the reference image to a template of the atlas, the template is fitted to the reference image as closely as possible and the pre-determined segmentation of the template image is then used as the segmentation for the reference image.
In fitting the template to the reference image, the size, shape and orientation of the template is changed until a difference between the reference image and the template is minimized. As both the reference image and the template image may be three-dimensional and high-resolution, there may be a very large number of degrees of freedom which must be simultaneously adjusted to find the optimal match. Accordingly, it may not be practical to attempt the performance of atlas-based segmentation at the pixel level, which is to say, attempting to match every pixel of the reference image to every pixel of the template image in one shot.
Accordingly, atlas-based segmentation may be performed in successive stages from a low-resolution stage to a high-resolution stage. In this approach, a low-resolution version of the template is first fitted to a low-resolution version of the reference image. Because of the reduced level of structural detail within the low-resolution images, fitting is significantly simplified. Then, the resolution of both the template and reference images are increased and fitting is repeated. However, here, the previous fitting is used as a starting point for the new fitting and as a result, the fitting process is simplified. This process of fitting using successively higher resolution images may be repeated for as many times as is necessary to produce the final high-resolution fitting, and from this final fitting, segmentation is achieved.
While this approach for atlas-bases segmentation may be effective, there is a need for more efficient and more accurate approaches for performing atlas-based segmentation.